Overview

Dataset statistics

Number of variables40
Number of observations2559
Missing cells550
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory663.9 B

Variable types

BOOL18
NUM16
CAT6

Warnings

marital_status has 302 (11.8%) missing values Missing
min_sbp has 41 (1.6%) missing values Missing
max_sbp has 41 (1.6%) missing values Missing
avg_sbp has 41 (1.6%) missing values Missing
hadm_id has unique values Unique
stay_id has unique values Unique
gcs_change has 1407 (55.0%) zeros Zeros

Reproduction

Analysis started2020-12-13 06:46:01.898961
Analysis finished2020-12-13 06:47:18.782568
Duration1 minute and 16.88 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

subject_id
Real number (ℝ≥0)

Distinct2511
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15054908.62
Minimum10004733
Maximum19999442
Zeros0
Zeros (%)0.0%
Memory size20.1 KiB
2020-12-13T14:47:19.003808image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum10004733
5-th percentile10527753.7
Q112492722
median15094658
Q317593421.5
95-th percentile19550207.9
Maximum19999442
Range9994709
Interquartile range (IQR)5100699.5

Descriptive statistics

Standard deviation2910369.169
Coefficient of variation (CV)0.1933169602
Kurtosis-1.232799807
Mean15054908.62
Median Absolute Deviation (MAD)2568667
Skewness-0.008265703963
Sum3.852551115e+10
Variance8.470248702e+12
MonotocityIncreasing
2020-12-13T14:47:19.208507image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1342576420.1%
 
1035011920.1%
 
1883445720.1%
 
1751004720.1%
 
1920949620.1%
 
1371425620.1%
 
1456972920.1%
 
1229118720.1%
 
1960941720.1%
 
1876354320.1%
 
1737512020.1%
 
1677357820.1%
 
1692510120.1%
 
1612100020.1%
 
1677084920.1%
 
1466663720.1%
 
1553736820.1%
 
1991891620.1%
 
1243811220.1%
 
1259028920.1%
 
1797515520.1%
 
1862340520.1%
 
1569634920.1%
 
1118170520.1%
 
1655073420.1%
 
Other values (2486)250998.0%
 
ValueCountFrequency (%) 
100047331< 0.1%
 
100237081< 0.1%
 
100249821< 0.1%
 
100307531< 0.1%
 
100510431< 0.1%
 
100538101< 0.1%
 
100722391< 0.1%
 
100738471< 0.1%
 
100892441< 0.1%
 
100922271< 0.1%
 
ValueCountFrequency (%) 
199994421< 0.1%
 
199893051< 0.1%
 
199877021< 0.1%
 
199856831< 0.1%
 
199854091< 0.1%
 
199831451< 0.1%
 
199803731< 0.1%
 
199658021< 0.1%
 
199563831< 0.1%
 
199506151< 0.1%
 

hadm_id
Real number (ℝ≥0)

UNIQUE

Distinct2559
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24904887.42
Minimum20005426
Maximum29999098
Zeros0
Zeros (%)0.0%
Memory size20.1 KiB
2020-12-13T14:47:19.481586image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum20005426
5-th percentile20543628.5
Q122399164.5
median24855970
Q327345654
95-th percentile29511913.3
Maximum29999098
Range9993672
Interquartile range (IQR)4946489.5

Descriptive statistics

Standard deviation2874065.969
Coefficient of variation (CV)0.1154016848
Kurtosis-1.185619012
Mean24904887.42
Median Absolute Deviation (MAD)2472011
Skewness0.06408313686
Sum6.37316069e+10
Variance8.260255194e+12
MonotocityNot monotonic
2020-12-13T14:47:19.688103image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
256009721< 0.1%
 
241219381< 0.1%
 
293778561< 0.1%
 
277107821< 0.1%
 
298509411< 0.1%
 
293901381< 0.1%
 
279703871< 0.1%
 
298714151< 0.1%
 
256156671< 0.1%
 
240480751< 0.1%
 
230465251< 0.1%
 
279012271< 0.1%
 
200102821< 0.1%
 
216384411< 0.1%
 
222159731< 0.1%
 
217019241< 0.1%
 
267317081< 0.1%
 
255193921< 0.1%
 
216875761< 0.1%
 
219597181< 0.1%
 
243619611< 0.1%
 
237526931< 0.1%
 
221566401< 0.1%
 
234325411< 0.1%
 
295115681< 0.1%
 
Other values (2534)253499.0%
 
ValueCountFrequency (%) 
200054261< 0.1%
 
200102821< 0.1%
 
200111171< 0.1%
 
200167141< 0.1%
 
200168811< 0.1%
 
200185551< 0.1%
 
200252081< 0.1%
 
200386381< 0.1%
 
200395861< 0.1%
 
200477621< 0.1%
 
ValueCountFrequency (%) 
299990981< 0.1%
 
299981151< 0.1%
 
299925771< 0.1%
 
299906501< 0.1%
 
299888761< 0.1%
 
299812611< 0.1%
 
299693841< 0.1%
 
299490281< 0.1%
 
299430511< 0.1%
 
299397611< 0.1%
 

admission_type
Categorical

Distinct8
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
EW EMER.
1387 
OBSERVATION ADMIT
511 
URGENT
460 
SURGICAL SAME DAY ADMISSION
 
91
DIRECT EMER.
 
51
Other values (3)
 
59
ValueCountFrequency (%) 
EW EMER.138754.2%
 
OBSERVATION ADMIT51120.0%
 
URGENT46018.0%
 
SURGICAL SAME DAY ADMISSION913.6%
 
DIRECT EMER.512.0%
 
ELECTIVE502.0%
 
EU OBSERVATION60.2%
 
DIRECT OBSERVATION30.1%
 
2020-12-13T14:47:19.882370image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-13T14:47:20.021611image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:47:20.203320image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length27
Median length8
Mean length10.21883548
Min length6

Overview of Unicode Properties

Unique unicode characters20
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
E554421.2%
 
R25639.8%
 
22318.5%
 
M21318.1%
 
T15956.1%
 
.14385.5%
 
I14085.4%
 
A13955.3%
 
W13875.3%
 
O11314.3%
 
N10714.1%
 
S8843.4%
 
D7472.9%
 
V5702.2%
 
U5572.1%
 
G5512.1%
 
B5202.0%
 
C1950.7%
 
L1410.5%
 
Y910.3%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter2248186.0%
 
Space Separator22318.5%
 
Other Punctuation14385.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E554424.7%
 
R256311.4%
 
M21319.5%
 
T15957.1%
 
I14086.3%
 
A13956.2%
 
W13876.2%
 
O11315.0%
 
N10714.8%
 
S8843.9%
 
D7473.3%
 
V5702.5%
 
U5572.5%
 
G5512.5%
 
B5202.3%
 
C1950.9%
 
L1410.6%
 
Y910.4%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
2231100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.1438100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin2248186.0%
 
Common366914.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
E554424.7%
 
R256311.4%
 
M21319.5%
 
T15957.1%
 
I14086.3%
 
A13956.2%
 
W13876.2%
 
O11315.0%
 
N10714.8%
 
S8843.9%
 
D7473.3%
 
V5702.5%
 
U5572.5%
 
G5512.5%
 
B5202.3%
 
C1950.9%
 
L1410.6%
 
Y910.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
223160.8%
 
.143839.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII26150100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
E554421.2%
 
R25639.8%
 
22318.5%
 
M21318.1%
 
T15956.1%
 
.14385.5%
 
I14085.4%
 
A13955.3%
 
W13875.3%
 
O11314.3%
 
N10714.1%
 
S8843.4%
 
D7472.9%
 
V5702.2%
 
U5572.1%
 
G5512.1%
 
B5202.0%
 
C1950.7%
 
L1410.5%
 
Y910.3%
 
Distinct10
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
EMERGENCY ROOM
1365 
TRANSFER FROM HOSPITAL
768 
PHYSICIAN REFERRAL
290 
WALK-IN/SELF REFERRAL
 
59
TRANSFER FROM SKILLED NURSING FACILITY
 
23
Other values (5)
 
54
ValueCountFrequency (%) 
EMERGENCY ROOM136553.3%
 
TRANSFER FROM HOSPITAL76830.0%
 
PHYSICIAN REFERRAL29011.3%
 
WALK-IN/SELF REFERRAL592.3%
 
TRANSFER FROM SKILLED NURSING FACILITY230.9%
 
CLINIC REFERRAL170.7%
 
INFORMATION NOT AVAILABLE170.7%
 
PROCEDURE SITE110.4%
 
PACU80.3%
 
AMBULATORY SURGERY TRANSFER1< 0.1%
 
2020-12-13T14:47:20.446045image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-12-13T14:47:20.576013image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:47:20.773885image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length38
Median length14
Mean length17.28487691
Min length4

Overview of Unicode Properties

Unique unicode characters25
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
R626814.2%
 
E575213.0%
 
O43529.8%
 
M35398.0%
 
34067.7%
 
N26205.9%
 
A23765.4%
 
F20484.6%
 
S19674.4%
 
C17313.9%
 
Y16803.8%
 
T16293.7%
 
I15953.6%
 
G13893.1%
 
L13733.1%
 
P10772.4%
 
H10582.4%
 
K820.2%
 
W590.1%
 
-590.1%
 
/590.1%
 
U440.1%
 
D340.1%
 
B18< 0.1%
 
V17< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter4070892.0%
 
Space Separator34067.7%
 
Dash Punctuation590.1%
 
Other Punctuation590.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
R626815.4%
 
E575214.1%
 
O435210.7%
 
M35398.7%
 
N26206.4%
 
A23765.8%
 
F20485.0%
 
S19674.8%
 
C17314.3%
 
Y16804.1%
 
T16294.0%
 
I15953.9%
 
G13893.4%
 
L13733.4%
 
P10772.6%
 
H10582.6%
 
K820.2%
 
W590.1%
 
U440.1%
 
D340.1%
 
B18< 0.1%
 
V17< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
3406100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-59100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/59100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin4070892.0%
 
Common35248.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
R626815.4%
 
E575214.1%
 
O435210.7%
 
M35398.7%
 
N26206.4%
 
A23765.8%
 
F20485.0%
 
S19674.8%
 
C17314.3%
 
Y16804.1%
 
T16294.0%
 
I15953.9%
 
G13893.4%
 
L13733.4%
 
P10772.6%
 
H10582.6%
 
K820.2%
 
W590.1%
 
U440.1%
 
D340.1%
 
B18< 0.1%
 
V17< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
340696.7%
 
-591.7%
 
/591.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII44232100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
R626814.2%
 
E575213.0%
 
O43529.8%
 
M35398.0%
 
34067.7%
 
N26205.9%
 
A23765.4%
 
F20484.6%
 
S19674.4%
 
C17313.9%
 
Y16803.8%
 
T16293.7%
 
I15953.6%
 
G13893.1%
 
L13733.1%
 
P10772.4%
 
H10582.4%
 
K820.2%
 
W590.1%
 
-590.1%
 
/590.1%
 
U440.1%
 
D340.1%
 
B18< 0.1%
 
V17< 0.1%
 
Distinct13
Distinct (%)0.5%
Missing16
Missing (%)0.6%
Memory size20.1 KiB
REHAB
757 
DIED
485 
SKILLED NURSING FACILITY
350 
HOME
310 
CHRONIC/LONG TERM ACUTE CARE
266 
Other values (8)
375 
ValueCountFrequency (%) 
REHAB75729.6%
 
DIED48519.0%
 
SKILLED NURSING FACILITY35013.7%
 
HOME31012.1%
 
CHRONIC/LONG TERM ACUTE CARE26610.4%
 
HOME HEALTH CARE2539.9%
 
HOSPICE933.6%
 
ACUTE HOSPITAL130.5%
 
AGAINST ADVICE80.3%
 
OTHER FACILITY40.2%
 
PSYCH FACILITY20.1%
 
HEALTHCARE FACILITY1< 0.1%
 
ASSISTED LIVING1< 0.1%
 
(Missing)160.6%
 
2020-12-13T14:47:21.018498image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)0.1%
2020-12-13T14:47:21.210497image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length28
Median length5
Mean length10.93083236
Min length3

Overview of Unicode Properties

Unique unicode characters25
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
E358012.8%
 
I22908.2%
 
H22067.9%
 
A22057.9%
 
R21637.7%
 
20337.3%
 
C17916.4%
 
L15915.7%
 
D13294.8%
 
N12414.4%
 
O12054.3%
 
T11824.2%
 
M8293.0%
 
S8192.9%
 
B7572.7%
 
U6292.2%
 
G6252.2%
 
Y3591.3%
 
F3571.3%
 
K3501.3%
 
/2661.0%
 
P1080.4%
 
n320.1%
 
a160.1%
 
V9< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter2562591.6%
 
Space Separator20337.3%
 
Other Punctuation2661.0%
 
Lowercase Letter480.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E358014.0%
 
I22908.9%
 
H22068.6%
 
A22058.6%
 
R21638.4%
 
C17917.0%
 
L15916.2%
 
D13295.2%
 
N12414.8%
 
O12054.7%
 
T11824.6%
 
M8293.2%
 
S8193.2%
 
B7573.0%
 
U6292.5%
 
G6252.4%
 
Y3591.4%
 
F3571.4%
 
K3501.4%
 
P1080.4%
 
V9< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
2033100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/266100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n3266.7%
 
a1633.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin2567391.8%
 
Common22998.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
E358013.9%
 
I22908.9%
 
H22068.6%
 
A22058.6%
 
R21638.4%
 
C17917.0%
 
L15916.2%
 
D13295.2%
 
N12414.8%
 
O12054.7%
 
T11824.6%
 
M8293.2%
 
S8193.2%
 
B7572.9%
 
U6292.5%
 
G6252.4%
 
Y3591.4%
 
F3571.4%
 
K3501.4%
 
P1080.4%
 
n320.1%
 
a160.1%
 
V9< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
203388.4%
 
/26611.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII27972100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
E358012.8%
 
I22908.2%
 
H22067.9%
 
A22057.9%
 
R21637.7%
 
20337.3%
 
C17916.4%
 
L15915.7%
 
D13294.8%
 
N12414.4%
 
O12054.3%
 
T11824.2%
 
M8293.0%
 
S8192.9%
 
B7572.7%
 
U6292.2%
 
G6252.2%
 
Y3591.3%
 
F3571.3%
 
K3501.3%
 
/2661.0%
 
P1080.4%
 
n320.1%
 
a160.1%
 
V9< 0.1%
 

marital_status
Categorical

MISSING

Distinct4
Distinct (%)0.2%
Missing302
Missing (%)11.8%
Memory size20.1 KiB
MARRIED
1065 
SINGLE
620 
WIDOWED
399 
DIVORCED
173 
ValueCountFrequency (%) 
MARRIED106541.6%
 
SINGLE62024.2%
 
WIDOWED39915.6%
 
DIVORCED1736.8%
 
(Missing)30211.8%
 
2020-12-13T14:47:21.383126image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-13T14:47:21.550813image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:47:21.699431image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length7
Mean length6.353262993
Min length3

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
R230314.2%
 
I225713.9%
 
E225713.9%
 
D220913.6%
 
M10656.6%
 
A10656.6%
 
W7984.9%
 
S6203.8%
 
N6203.8%
 
G6203.8%
 
L6203.8%
 
n6043.7%
 
O5723.5%
 
a3021.9%
 
V1731.1%
 
C1731.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter1535294.4%
 
Lowercase Letter9065.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
R230315.0%
 
I225714.7%
 
E225714.7%
 
D220914.4%
 
M10656.9%
 
A10656.9%
 
W7985.2%
 
S6204.0%
 
N6204.0%
 
G6204.0%
 
L6204.0%
 
O5723.7%
 
V1731.1%
 
C1731.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n60466.7%
 
a30233.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin16258100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
R230314.2%
 
I225713.9%
 
E225713.9%
 
D220913.6%
 
M10656.6%
 
A10656.6%
 
W7984.9%
 
S6203.8%
 
N6203.8%
 
G6203.8%
 
L6203.8%
 
n6043.7%
 
O5723.5%
 
a3021.9%
 
V1731.1%
 
C1731.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII16258100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
R230314.2%
 
I225713.9%
 
E225713.9%
 
D220913.6%
 
M10656.6%
 
A10656.6%
 
W7984.9%
 
S6203.8%
 
N6203.8%
 
G6203.8%
 
L6203.8%
 
n6043.7%
 
O5723.5%
 
a3021.9%
 
V1731.1%
 
C1731.1%
 

ethnicity
Categorical

Distinct8
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
WHITE
1609 
UNKNOWN
360 
BLACK/AFRICAN AMERICAN
263 
OTHER
 
127
HISPANIC/LATINO
 
91
Other values (3)
 
109
ValueCountFrequency (%) 
WHITE160962.9%
 
UNKNOWN36014.1%
 
BLACK/AFRICAN AMERICAN26310.3%
 
OTHER1275.0%
 
HISPANIC/LATINO913.6%
 
ASIAN763.0%
 
UNABLE TO OBTAIN251.0%
 
AMERICAN INDIAN/ALASKA NATIVE80.3%
 
2020-12-13T14:47:21.964314image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-13T14:47:22.116711image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:47:22.299474image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length29
Median length5
Mean length7.566627589
Min length5

Overview of Unicode Properties

Unique unicode characters22
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
I254113.1%
 
E204010.5%
 
W196910.2%
 
N194610.1%
 
T18859.7%
 
H18279.4%
 
A17559.1%
 
C8884.6%
 
R6613.4%
 
K6313.3%
 
O6283.2%
 
L3872.0%
 
U3852.0%
 
/3621.9%
 
3291.7%
 
B3131.6%
 
M2711.4%
 
F2631.4%
 
S1750.9%
 
P910.5%
 
D8< 0.1%
 
V8< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter1867296.4%
 
Other Punctuation3621.9%
 
Space Separator3291.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
I254113.6%
 
E204010.9%
 
W196910.5%
 
N194610.4%
 
T188510.1%
 
H18279.8%
 
A17559.4%
 
C8884.8%
 
R6613.5%
 
K6313.4%
 
O6283.4%
 
L3872.1%
 
U3852.1%
 
B3131.7%
 
M2711.5%
 
F2631.4%
 
S1750.9%
 
P910.5%
 
D8< 0.1%
 
V8< 0.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/362100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
329100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1867296.4%
 
Common6913.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
I254113.6%
 
E204010.9%
 
W196910.5%
 
N194610.4%
 
T188510.1%
 
H18279.8%
 
A17559.4%
 
C8884.8%
 
R6613.5%
 
K6313.4%
 
O6283.4%
 
L3872.1%
 
U3852.1%
 
B3131.7%
 
M2711.5%
 
F2631.4%
 
S1750.9%
 
P910.5%
 
D8< 0.1%
 
V8< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
/36252.4%
 
32947.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII19363100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
I254113.1%
 
E204010.5%
 
W196910.2%
 
N194610.1%
 
T18859.7%
 
H18279.4%
 
A17559.1%
 
C8884.6%
 
R6613.4%
 
K6313.3%
 
O6283.2%
 
L3872.0%
 
U3852.0%
 
/3621.9%
 
3291.7%
 
B3131.6%
 
M2711.4%
 
F2631.4%
 
S1750.9%
 
P910.5%
 
D8< 0.1%
 
V8< 0.1%
 
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
0
2074 
1
485 
ValueCountFrequency (%) 
0207481.0%
 
148519.0%
 
2020-12-13T14:47:22.470038image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

gender
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
F
1290 
M
1269 
ValueCountFrequency (%) 
F129050.4%
 
M126949.6%
 
2020-12-13T14:47:22.614760image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-13T14:47:22.756152image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:47:22.955638image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
F129050.4%
 
M126949.6%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter2559100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
F129050.4%
 
M126949.6%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin2559100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
F129050.4%
 
M126949.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2559100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
F129050.4%
 
M126949.6%
 

stay_id
Real number (ℝ≥0)

UNIQUE

Distinct2559
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34920037.88
Minimum30001388
Maximum39997976
Zeros0
Zeros (%)0.0%
Memory size20.1 KiB
2020-12-13T14:47:23.164114image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum30001388
5-th percentile30484506
Q132399545
median34888894
Q337370210
95-th percentile39433146.5
Maximum39997976
Range9996588
Interquartile range (IQR)4970665

Descriptive statistics

Standard deviation2883566.218
Coefficient of variation (CV)0.08257626259
Kurtosis-1.213120909
Mean34920037.88
Median Absolute Deviation (MAD)2487869
Skewness0.03326175685
Sum8.936037694e+10
Variance8.314954133e+12
MonotocityNot monotonic
2020-12-13T14:47:23.377849image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
386939021< 0.1%
 
378712401< 0.1%
 
305677511< 0.1%
 
389993661< 0.1%
 
358700201< 0.1%
 
380224671< 0.1%
 
346330221< 0.1%
 
336534441< 0.1%
 
348460271< 0.1%
 
329270331< 0.1%
 
317269211< 0.1%
 
327734301< 0.1%
 
377070611< 0.1%
 
388375551< 0.1%
 
321754081< 0.1%
 
381911091< 0.1%
 
327631811< 0.1%
 
331195311< 0.1%
 
395441361< 0.1%
 
304919781< 0.1%
 
354386391< 0.1%
 
380123841< 0.1%
 
329864661< 0.1%
 
351716811< 0.1%
 
326137281< 0.1%
 
Other values (2534)253499.0%
 
ValueCountFrequency (%) 
300013881< 0.1%
 
300032431< 0.1%
 
300073071< 0.1%
 
300082521< 0.1%
 
300136751< 0.1%
 
300149971< 0.1%
 
300270521< 0.1%
 
300309131< 0.1%
 
300321831< 0.1%
 
300323761< 0.1%
 
ValueCountFrequency (%) 
399979761< 0.1%
 
399928101< 0.1%
 
399906051< 0.1%
 
399895321< 0.1%
 
399843531< 0.1%
 
399764601< 0.1%
 
399555921< 0.1%
 
399550761< 0.1%
 
399441171< 0.1%
 
399441061< 0.1%
 

los
Real number (ℝ≥0)

Distinct2548
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3807134
Minimum0.09041666667
Maximum84.38168981
Zeros0
Zeros (%)0.0%
Memory size20.1 KiB
2020-12-13T14:47:23.589989image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.09041666667
5-th percentile0.7850289352
Q11.738958333
median3.404768519
Q37.732008102
95-th percentile21.1769213
Maximum84.38168981
Range84.29127315
Interquartile range (IQR)5.993049769

Descriptive statistics

Standard deviation8.079556118
Coefficient of variation (CV)1.266246517
Kurtosis17.54547046
Mean6.3807134
Median Absolute Deviation (MAD)2.232939815
Skewness3.406533269
Sum16328.24559
Variance65.27922707
MonotocityNot monotonic
2020-12-13T14:47:23.787357image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.69616898120.1%
 
1.26111111120.1%
 
1.04261574120.1%
 
3.1216203720.1%
 
1.83935185220.1%
 
1.73651620420.1%
 
0.579675925920.1%
 
1.75034722220.1%
 
4.76151620420.1%
 
2.29461805620.1%
 
2.21012731520.1%
 
3.3225231481< 0.1%
 
2.8043981481< 0.1%
 
0.81697916671< 0.1%
 
4.1756365741< 0.1%
 
15.212407411< 0.1%
 
1.7768402781< 0.1%
 
1.1405555561< 0.1%
 
1.7374189811< 0.1%
 
11.850289351< 0.1%
 
3.0081018521< 0.1%
 
1.92031251< 0.1%
 
2.1041782411< 0.1%
 
17.667361111< 0.1%
 
7.3781944441< 0.1%
 
Other values (2523)252398.6%
 
ValueCountFrequency (%) 
0.090416666671< 0.1%
 
0.09964120371< 0.1%
 
0.12550925931< 0.1%
 
0.15652777781< 0.1%
 
0.19695601851< 0.1%
 
0.22086805561< 0.1%
 
0.22756944441< 0.1%
 
0.23223379631< 0.1%
 
0.23748842591< 0.1%
 
0.24065972221< 0.1%
 
ValueCountFrequency (%) 
84.381689811< 0.1%
 
82.833738431< 0.1%
 
75.099224541< 0.1%
 
66.584236111< 0.1%
 
61.873888891< 0.1%
 
60.407673611< 0.1%
 
58.096099541< 0.1%
 
56.490879631< 0.1%
 
55.720219911< 0.1%
 
55.574282411< 0.1%
 

age
Real number (ℝ≥0)

Distinct71
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.55920281
Minimum18
Maximum89
Zeros0
Zeros (%)0.0%
Memory size20.1 KiB
2020-12-13T14:47:24.039456image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile39
Q158
median70
Q382
95-th percentile89
Maximum89
Range71
Interquartile range (IQR)24

Descriptive statistics

Standard deviation15.75199679
Coefficient of variation (CV)0.2297575839
Kurtosis-0.0514331272
Mean68.55920281
Median Absolute Deviation (MAD)12
Skewness-0.6841743528
Sum175443
Variance248.125403
MonotocityNot monotonic
2020-12-13T14:47:24.244406image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
892519.8%
 
69783.0%
 
82722.8%
 
65642.5%
 
77632.5%
 
74612.4%
 
71612.4%
 
66602.3%
 
72602.3%
 
63602.3%
 
88602.3%
 
79602.3%
 
68592.3%
 
84592.3%
 
85582.3%
 
78582.3%
 
87572.2%
 
83562.2%
 
80562.2%
 
67542.1%
 
75542.1%
 
60502.0%
 
76502.0%
 
64481.9%
 
73481.9%
 
Other values (46)90235.2%
 
ValueCountFrequency (%) 
181< 0.1%
 
1920.1%
 
2020.1%
 
2150.2%
 
2220.1%
 
2380.3%
 
2440.2%
 
2560.2%
 
2640.2%
 
2760.2%
 
ValueCountFrequency (%) 
892519.8%
 
88602.3%
 
87572.2%
 
86461.8%
 
85582.3%
 
84592.3%
 
83562.2%
 
82722.8%
 
81471.8%
 
80562.2%
 
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
0
2338 
1
 
221
ValueCountFrequency (%) 
0233891.4%
 
12218.6%
 
2020-12-13T14:47:24.449975image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

min_mbp
Real number (ℝ≥0)

Distinct126
Distinct (%)5.0%
Missing25
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean72.80939227
Minimum1
Maximum133
Zeros0
Zeros (%)0.0%
Memory size20.1 KiB
2020-12-13T14:47:24.592160image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile49
Q163
median73
Q383
95-th percentile99
Maximum133
Range132
Interquartile range (IQR)20

Descriptive statistics

Standard deviation16.26764202
Coefficient of variation (CV)0.2234277958
Kurtosis1.659323037
Mean72.80939227
Median Absolute Deviation (MAD)10
Skewness-0.4496431174
Sum184499
Variance264.636177
MonotocityNot monotonic
2020-12-13T14:47:24.808175image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
72742.9%
 
68742.9%
 
75742.9%
 
76662.6%
 
63662.6%
 
73652.5%
 
65642.5%
 
67632.5%
 
71622.4%
 
62622.4%
 
74622.4%
 
79612.4%
 
66612.4%
 
81612.4%
 
69592.3%
 
83582.3%
 
78572.2%
 
60562.2%
 
77562.2%
 
87552.1%
 
70552.1%
 
80522.0%
 
84512.0%
 
82461.8%
 
64461.8%
 
Other values (101)102840.2%
 
ValueCountFrequency (%) 
120.1%
 
31< 0.1%
 
41< 0.1%
 
51< 0.1%
 
640.2%
 
71< 0.1%
 
920.1%
 
1030.1%
 
1120.1%
 
1430.1%
 
ValueCountFrequency (%) 
1331< 0.1%
 
1231< 0.1%
 
1211< 0.1%
 
11920.1%
 
1161< 0.1%
 
1151< 0.1%
 
1141< 0.1%
 
11350.2%
 
11220.1%
 
11140.2%
 

max_mbp
Real number (ℝ≥0)

Distinct141
Distinct (%)5.6%
Missing25
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean104.51618
Minimum53
Maximum295
Zeros0
Zeros (%)0.0%
Memory size20.1 KiB
2020-12-13T14:47:25.022218image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum53
5-th percentile76
Q191
median103
Q3115
95-th percentile138.175
Maximum295
Range242
Interquartile range (IQR)24

Descriptive statistics

Standard deviation20.46611246
Coefficient of variation (CV)0.1958176473
Kurtosis6.575453002
Mean104.51618
Median Absolute Deviation (MAD)12
Skewness1.351441064
Sum264844
Variance418.8617594
MonotocityNot monotonic
2020-12-13T14:47:25.228629image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
99652.5%
 
105622.4%
 
104612.4%
 
107592.3%
 
112582.3%
 
108572.2%
 
97572.2%
 
95572.2%
 
94572.2%
 
110552.1%
 
98552.1%
 
109542.1%
 
96542.1%
 
100522.0%
 
102502.0%
 
90491.9%
 
93491.9%
 
101481.9%
 
103471.8%
 
87461.8%
 
115451.8%
 
86451.8%
 
111431.7%
 
88421.6%
 
84421.6%
 
Other values (116)122547.9%
 
ValueCountFrequency (%) 
531< 0.1%
 
541< 0.1%
 
571< 0.1%
 
6030.1%
 
6130.1%
 
6440.2%
 
6530.1%
 
6640.2%
 
6760.2%
 
6870.3%
 
ValueCountFrequency (%) 
2951< 0.1%
 
2571< 0.1%
 
2561< 0.1%
 
2191< 0.1%
 
212.51< 0.1%
 
2041< 0.1%
 
1991< 0.1%
 
1981< 0.1%
 
1971< 0.1%
 
1961< 0.1%
 

avg_mbp
Real number (ℝ≥0)

Distinct1313
Distinct (%)51.8%
Missing25
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean87.21923183
Minimum45.625
Maximum141.16
Zeros0
Zeros (%)0.0%
Memory size20.1 KiB
2020-12-13T14:47:25.495468image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum45.625
5-th percentile65.54642857
Q177.14285714
median86.71428571
Q396.71428571
95-th percentile111.1958333
Maximum141.16
Range95.535
Interquartile range (IQR)19.57142857

Descriptive statistics

Standard deviation14.03295876
Coefficient of variation (CV)0.1608929415
Kurtosis-0.1949442262
Mean87.21923183
Median Absolute Deviation (MAD)9.857142857
Skewness0.2309985282
Sum221013.5335
Variance196.9239316
MonotocityNot monotonic
2020-12-13T14:47:25.772066image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
86140.5%
 
89120.5%
 
90120.5%
 
88120.5%
 
87120.5%
 
95110.4%
 
75110.4%
 
94100.4%
 
9790.4%
 
8590.4%
 
8490.4%
 
9290.4%
 
7990.4%
 
9190.4%
 
9380.3%
 
9980.3%
 
81.2857142980.3%
 
10280.3%
 
6480.3%
 
6780.3%
 
76.6666666770.3%
 
10670.3%
 
95.1666666770.3%
 
7670.3%
 
8170.3%
 
Other values (1288)230390.0%
 
(Missing)251.0%
 
ValueCountFrequency (%) 
45.6251< 0.1%
 
47.142857141< 0.1%
 
47.51< 0.1%
 
50.923076921< 0.1%
 
53.520.1%
 
53.555555561< 0.1%
 
53.61< 0.1%
 
541< 0.1%
 
54.285714291< 0.1%
 
54.333333331< 0.1%
 
ValueCountFrequency (%) 
141.161< 0.1%
 
1331< 0.1%
 
132.51< 0.1%
 
132.29166671< 0.1%
 
130.18751< 0.1%
 
1271< 0.1%
 
1261< 0.1%
 
125.51< 0.1%
 
125.42857141< 0.1%
 
125.156251< 0.1%
 

min_sbp
Real number (ℝ≥0)

MISSING

Distinct144
Distinct (%)5.7%
Missing41
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean112.9372518
Minimum22
Maximum196
Zeros0
Zeros (%)0.0%
Memory size20.1 KiB
2020-12-13T14:47:26.066849image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile78
Q197
median112
Q3128
95-th percentile151
Maximum196
Range174
Interquartile range (IQR)31

Descriptive statistics

Standard deviation22.33241199
Coefficient of variation (CV)0.1977417693
Kurtosis0.1187594321
Mean112.9372518
Median Absolute Deviation (MAD)15
Skewness0.04831194082
Sum284376
Variance498.7366253
MonotocityNot monotonic
2020-12-13T14:47:26.271103image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
118582.3%
 
103572.2%
 
112491.9%
 
115491.9%
 
107481.9%
 
105481.9%
 
110471.8%
 
94461.8%
 
120441.7%
 
121441.7%
 
108441.7%
 
116441.7%
 
95431.7%
 
123431.7%
 
122421.6%
 
102411.6%
 
117411.6%
 
113401.6%
 
109401.6%
 
97401.6%
 
98391.5%
 
119391.5%
 
106391.5%
 
111381.5%
 
114361.4%
 
Other values (119)141955.5%
 
(Missing)411.6%
 
ValueCountFrequency (%) 
221< 0.1%
 
321< 0.1%
 
331< 0.1%
 
411< 0.1%
 
4520.1%
 
481< 0.1%
 
4920.1%
 
5020.1%
 
5220.1%
 
5550.2%
 
ValueCountFrequency (%) 
1961< 0.1%
 
1951< 0.1%
 
1861< 0.1%
 
18420.1%
 
1781< 0.1%
 
1771< 0.1%
 
17420.1%
 
1711< 0.1%
 
1701< 0.1%
 
16840.2%
 

max_sbp
Real number (ℝ≥0)

MISSING

Distinct154
Distinct (%)6.1%
Missing41
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean152.7716442
Minimum78
Maximum318
Zeros0
Zeros (%)0.0%
Memory size20.1 KiB
2020-12-13T14:47:26.532661image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum78
5-th percentile113
Q1135
median152
Q3170
95-th percentile195
Maximum318
Range240
Interquartile range (IQR)35

Descriptive statistics

Standard deviation25.49779097
Coefficient of variation (CV)0.1669013324
Kurtosis1.018310242
Mean152.7716442
Median Absolute Deviation (MAD)18
Skewness0.4584045829
Sum384679
Variance650.1373442
MonotocityNot monotonic
2020-12-13T14:47:26.735809image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
133481.9%
 
142471.8%
 
156461.8%
 
155451.8%
 
138441.7%
 
149431.7%
 
148411.6%
 
144411.6%
 
145401.6%
 
171391.5%
 
158381.5%
 
135381.5%
 
162381.5%
 
147381.5%
 
153371.4%
 
154371.4%
 
160361.4%
 
130361.4%
 
169361.4%
 
150361.4%
 
146361.4%
 
161351.4%
 
139351.4%
 
170351.4%
 
152341.3%
 
Other values (129)153960.1%
 
(Missing)411.6%
 
ValueCountFrequency (%) 
781< 0.1%
 
9220.1%
 
92.51< 0.1%
 
941< 0.1%
 
9520.1%
 
961< 0.1%
 
9730.1%
 
9860.2%
 
9950.2%
 
1001< 0.1%
 
ValueCountFrequency (%) 
3181< 0.1%
 
2841< 0.1%
 
2651< 0.1%
 
25420.1%
 
2461< 0.1%
 
2451< 0.1%
 
2431< 0.1%
 
2401< 0.1%
 
2381< 0.1%
 
2351< 0.1%
 

avg_sbp
Real number (ℝ≥0)

MISSING

Distinct1534
Distinct (%)60.9%
Missing41
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean132.3811127
Minimum75
Maximum207.7857143
Zeros0
Zeros (%)0.0%
Memory size20.1 KiB
2020-12-13T14:47:27.014133image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum75
5-th percentile100.085
Q1116.6666667
median131.8333333
Q3147.2
95-th percentile167.05
Maximum207.7857143
Range132.7857143
Interquartile range (IQR)30.53333333

Descriptive statistics

Standard deviation21.00627048
Coefficient of variation (CV)0.1586802683
Kurtosis-0.2510078657
Mean132.3811127
Median Absolute Deviation (MAD)15.32142857
Skewness0.2287513005
Sum333335.6418
Variance441.2633994
MonotocityNot monotonic
2020-12-13T14:47:27.233602image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
120100.4%
 
147100.4%
 
111100.4%
 
13190.4%
 
11490.4%
 
13280.3%
 
12580.3%
 
15680.3%
 
12670.3%
 
12270.3%
 
13060.2%
 
13760.2%
 
141.2560.2%
 
119.560.2%
 
15360.2%
 
10860.2%
 
15760.2%
 
126.833333360.2%
 
14360.2%
 
137.285714360.2%
 
100.560.2%
 
12860.2%
 
12760.2%
 
124.666666760.2%
 
129.550.2%
 
Other values (1509)234391.6%
 
(Missing)411.6%
 
ValueCountFrequency (%) 
751< 0.1%
 
76.5751< 0.1%
 
771< 0.1%
 
82.8751< 0.1%
 
841< 0.1%
 
84.818181821< 0.1%
 
84.833333331< 0.1%
 
85.714285711< 0.1%
 
861< 0.1%
 
86.520.1%
 
ValueCountFrequency (%) 
207.78571431< 0.1%
 
202.45454551< 0.1%
 
202.16666671< 0.1%
 
201.05555561< 0.1%
 
2011< 0.1%
 
198.85714291< 0.1%
 
198.68751< 0.1%
 
1961< 0.1%
 
195.21< 0.1%
 
194.33333331< 0.1%
 

first_gcs
Real number (ℝ≥0)

Distinct13
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.88159437
Minimum3
Maximum15
Zeros0
Zeros (%)0.0%
Memory size20.1 KiB
2020-12-13T14:47:27.467928image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile9
Q114
median15
Q315
95-th percentile15
Maximum15
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.28065282
Coefficient of variation (CV)0.1642932907
Kurtosis7.25703625
Mean13.88159437
Median Absolute Deviation (MAD)0
Skewness-2.6318877
Sum35523
Variance5.201377285
MonotocityNot monotonic
2020-12-13T14:47:27.648005image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
15173267.7%
 
1427310.7%
 
131455.7%
 
12923.6%
 
11853.3%
 
10672.6%
 
9471.8%
 
8381.5%
 
3291.1%
 
7251.0%
 
6140.5%
 
560.2%
 
460.2%
 
ValueCountFrequency (%) 
3291.1%
 
460.2%
 
560.2%
 
6140.5%
 
7251.0%
 
8381.5%
 
9471.8%
 
10672.6%
 
11853.3%
 
12923.6%
 
ValueCountFrequency (%) 
15173267.7%
 
1427310.7%
 
131455.7%
 
12923.6%
 
11853.3%
 
10672.6%
 
9471.8%
 
8381.5%
 
7251.0%
 
6140.5%
 

last_gcs
Real number (ℝ≥0)

Distinct13
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.52364205
Minimum3
Maximum15
Zeros0
Zeros (%)0.0%
Memory size20.1 KiB
2020-12-13T14:47:27.802016image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile7
Q113
median15
Q315
95-th percentile15
Maximum15
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.738564228
Coefficient of variation (CV)0.2025019753
Kurtosis4.843128747
Mean13.52364205
Median Absolute Deviation (MAD)0
Skewness-2.299786428
Sum34607
Variance7.499734033
MonotocityNot monotonic
2020-12-13T14:47:28.067071image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
15155160.6%
 
1435313.8%
 
131586.2%
 
12933.6%
 
11913.6%
 
10783.0%
 
3542.1%
 
9481.9%
 
8411.6%
 
7291.1%
 
6271.1%
 
5180.7%
 
4180.7%
 
ValueCountFrequency (%) 
3542.1%
 
4180.7%
 
5180.7%
 
6271.1%
 
7291.1%
 
8411.6%
 
9481.9%
 
10783.0%
 
11913.6%
 
12933.6%
 
ValueCountFrequency (%) 
15155160.6%
 
1435313.8%
 
131586.2%
 
12933.6%
 
11913.6%
 
10783.0%
 
9481.9%
 
8411.6%
 
7291.1%
 
6271.1%
 

gcs_change
Real number (ℝ)

ZEROS

Distinct25
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.3579523251
Minimum-12
Maximum12
Zeros1407
Zeros (%)55.0%
Memory size20.1 KiB
2020-12-13T14:47:28.232342image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-12
5-th percentile-7
Q1-1
median0
Q30
95-th percentile4
Maximum12
Range24
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.0894987
Coefficient of variation (CV)-8.631034032
Kurtosis5.487480702
Mean-0.3579523251
Median Absolute Deviation (MAD)0
Skewness-0.7633236567
Sum-916
Variance9.545002217
MonotocityNot monotonic
2020-12-13T14:47:28.422037image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%) 
0140755.0%
 
-125610.0%
 
11947.6%
 
21044.1%
 
-2873.4%
 
3622.4%
 
-4542.1%
 
-5491.9%
 
-3491.9%
 
4431.7%
 
-12401.6%
 
-7301.2%
 
5291.1%
 
-6240.9%
 
-9180.7%
 
-8170.7%
 
7160.6%
 
12150.6%
 
6130.5%
 
8130.5%
 
-11130.5%
 
-10120.5%
 
970.3%
 
1150.2%
 
1020.1%
 
ValueCountFrequency (%) 
-12401.6%
 
-11130.5%
 
-10120.5%
 
-9180.7%
 
-8170.7%
 
-7301.2%
 
-6240.9%
 
-5491.9%
 
-4542.1%
 
-3491.9%
 
ValueCountFrequency (%) 
12150.6%
 
1150.2%
 
1020.1%
 
970.3%
 
8130.5%
 
7160.6%
 
6130.5%
 
5291.1%
 
4431.7%
 
3622.4%
 

afib
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
0
2049 
1
510 
ValueCountFrequency (%) 
0204980.1%
 
151019.9%
 
2020-12-13T14:47:28.554739image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
0
1407 
1
1152 
ValueCountFrequency (%) 
0140755.0%
 
1115245.0%
 
2020-12-13T14:47:28.642833image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

diabetes
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
0
1727 
1
832 
ValueCountFrequency (%) 
0172767.5%
 
183232.5%
 
2020-12-13T14:47:28.730609image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
1
1372 
0
1187 
ValueCountFrequency (%) 
1137253.6%
 
0118746.4%
 
2020-12-13T14:47:28.820231image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

cor_art_d
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
0
1713 
1
846 
ValueCountFrequency (%) 
0171366.9%
 
184633.1%
 
2020-12-13T14:47:28.977520image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
0
2411 
1
 
148
ValueCountFrequency (%) 
0241194.2%
 
11485.8%
 
2020-12-13T14:47:29.075471image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
0
2376 
1
 
183
ValueCountFrequency (%) 
0237692.8%
 
11837.2%
 
2020-12-13T14:47:29.171605image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

smoking
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
0
1874 
1
685 
ValueCountFrequency (%) 
0187473.2%
 
168526.8%
 
2020-12-13T14:47:29.256613image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

tia
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
0
2219 
1
340 
ValueCountFrequency (%) 
0221986.7%
 
134013.3%
 
2020-12-13T14:47:29.357153image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

heparin_iv
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
0
2082 
1
477 
ValueCountFrequency (%) 
0208281.4%
 
147718.6%
 
2020-12-13T14:47:29.476238image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
0
2354 
1
 
205
ValueCountFrequency (%) 
0235492.0%
 
12058.0%
 
2020-12-13T14:47:29.563922image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
0
2209 
1
350 
ValueCountFrequency (%) 
0220986.3%
 
135013.7%
 
2020-12-13T14:47:29.660677image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

inotropes
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
0
2130 
1
429 
ValueCountFrequency (%) 
0213083.2%
 
142916.8%
 
2020-12-13T14:47:29.757525image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
0
1819 
1
740 
ValueCountFrequency (%) 
0181971.1%
 
174028.9%
 
2020-12-13T14:47:29.856051image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

anticoag
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
0
2456 
1
 
103
ValueCountFrequency (%) 
0245696.0%
 
11034.0%
 
2020-12-13T14:47:30.000336image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
0
2157 
1
402 
ValueCountFrequency (%) 
0215784.3%
 
140215.7%
 
2020-12-13T14:47:30.098245image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

first_glu
Real number (ℝ≥0)

Distinct308
Distinct (%)12.1%
Missing17
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean144.3741149
Minimum28
Maximum1293
Zeros0
Zeros (%)0.0%
Memory size20.1 KiB
2020-12-13T14:47:30.269678image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile84
Q1101
median123
Q3160
95-th percentile286.9
Maximum1293
Range1265
Interquartile range (IQR)59

Descriptive statistics

Standard deviation76.89795345
Coefficient of variation (CV)0.5326297828
Kurtosis40.43800794
Mean144.3741149
Median Absolute Deviation (MAD)25
Skewness4.498585126
Sum366999
Variance5913.295245
MonotocityNot monotonic
2020-12-13T14:47:30.590083image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
105421.6%
 
92401.6%
 
104391.5%
 
107381.5%
 
99381.5%
 
100371.4%
 
115361.4%
 
106351.4%
 
103351.4%
 
96351.4%
 
95341.3%
 
111341.3%
 
94321.3%
 
118321.3%
 
98311.2%
 
114311.2%
 
102311.2%
 
89301.2%
 
110301.2%
 
97291.1%
 
101291.1%
 
116291.1%
 
126291.1%
 
93291.1%
 
135291.1%
 
Other values (283)170866.7%
 
ValueCountFrequency (%) 
281< 0.1%
 
361< 0.1%
 
511< 0.1%
 
541< 0.1%
 
5730.1%
 
591< 0.1%
 
601< 0.1%
 
631< 0.1%
 
6540.2%
 
6620.1%
 
ValueCountFrequency (%) 
12931< 0.1%
 
11251< 0.1%
 
8451< 0.1%
 
8221< 0.1%
 
7461< 0.1%
 
6601< 0.1%
 
6071< 0.1%
 
6061< 0.1%
 
5861< 0.1%
 
5621< 0.1%
 

first_cre
Real number (ℝ≥0)

Distinct85
Distinct (%)3.3%
Missing17
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean1.27675059
Minimum0.2
Maximum33.1
Zeros0
Zeros (%)0.0%
Memory size20.1 KiB
2020-12-13T14:47:30.938267image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.5
Q10.7
median0.9
Q31.3
95-th percentile2.9
Maximum33.1
Range32.9
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation1.486737374
Coefficient of variation (CV)1.164469698
Kurtosis117.5719018
Mean1.27675059
Median Absolute Deviation (MAD)0.2
Skewness8.432348182
Sum3245.5
Variance2.21038802
MonotocityNot monotonic
2020-12-13T14:47:31.161815image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.834913.6%
 
0.731812.4%
 
0.929611.6%
 
12499.7%
 
0.62208.6%
 
1.11787.0%
 
1.21445.6%
 
0.51134.4%
 
1.4953.7%
 
1.3843.3%
 
1.5602.3%
 
1.6461.8%
 
1.8391.5%
 
1.7351.4%
 
2291.1%
 
1.9291.1%
 
0.4220.9%
 
2.3210.8%
 
2.4150.6%
 
2.1110.4%
 
2.2110.4%
 
0.3100.4%
 
2.590.4%
 
2.680.3%
 
2.880.3%
 
Other values (60)1435.6%
 
(Missing)170.7%
 
ValueCountFrequency (%) 
0.230.1%
 
0.3100.4%
 
0.4220.9%
 
0.51134.4%
 
0.62208.6%
 
0.731812.4%
 
0.834913.6%
 
0.929611.6%
 
12499.7%
 
1.11787.0%
 
ValueCountFrequency (%) 
33.11< 0.1%
 
20.11< 0.1%
 
18.21< 0.1%
 
15.520.1%
 
13.81< 0.1%
 
13.21< 0.1%
 
12.61< 0.1%
 
11.81< 0.1%
 
11.320.1%
 
10.920.1%
 

Interactions

2020-12-13T14:46:23.345988image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:46:24.185069image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:46:24.728334image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:46:25.089347image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:46:25.319421image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:46:25.986048image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:46:26.485238image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:46:27.079333image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:46:27.944396image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:46:28.260208image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:46:28.877085image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:46:29.607980image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:46:29.990166image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:46:30.581195image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:46:31.237411image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:46:31.764625image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:46:31.953766image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:46:32.255036image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:46:32.584551image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:46:33.046031image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:46:33.409197image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:46:33.637692image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:46:33.837486image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:46:34.136271image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:46:34.523319image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:46:34.859823image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:46:35.353961image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:46:36.006825image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:46:36.228660image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:46:36.407182image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:46:36.580935image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:46:36.755060image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:46:36.921083image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:46:37.116386image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2020-12-13T14:47:13.403660image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:47:13.562235image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:47:13.726733image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:47:13.908533image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:47:14.118748image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:47:14.303072image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:47:14.471435image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:47:14.641144image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:47:14.884653image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:47:15.063750image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:47:15.218514image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:47:15.389779image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:47:15.575947image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:47:15.754059image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:47:15.939054image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:47:16.105912image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-12-13T14:47:31.463195image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-13T14:47:31.905218image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-13T14:47:32.324343image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-13T14:47:32.791474image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-12-13T14:47:33.171676image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-12-13T14:47:16.667038image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:47:17.745094image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:47:18.185954image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-13T14:47:18.550071image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

subject_idhadm_idadmission_typeadmission_locationdischarge_locationmarital_statusethnicityhospital_expire_flaggenderstay_idlosageinpatient_strokemin_mbpmax_mbpavg_mbpmin_sbpmax_sbpavg_sbpfirst_gcslast_gcsgcs_changeafibhyperlipidemiadiabeteshypertensioncor_art_dperi_vasc_dcar_art_stentsmokingtiaheparin_ivantihypertensive_iv_non_tight_controlantihypertensive_iv_tight_controlinotropesantiplateletsanticoagantihypertensive_non_ivfirst_glufirst_cre
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11002370821451830OBSERVATION ADMITWALK-IN/SELF REFERRALCHRONIC/LONG TERM ACUTE CAREWIDOWEDWHITE0F382493384.15951489078.095.088.250000156.0174.0165.000000141510111000000000101147.00.8
21002498225154057EW EMER.EMERGENCY ROOMDIEDMARRIEDWHITE1M3634167421.17483885155.0106.079.57142983.0160.0117.714286153-12100010011100111094.01.4
31003075327165162EW EMER.TRANSFER FROM HOSPITALSKILLED NURSING FACILITYDIVORCEDWHITE0F314272356.72756957066.078.072.166667103.0140.0118.166667151500111100111100100161.02.3
41005104322009252URGENTTRANSFER FROM HOSPITALHOMEDIVORCEDWHITE0F316525241.43827564092.0111.0101.85714399.0161.0138.28571415150100000000000010089.01.4
51005381026647692EW EMER.TRANSFER FROM HOSPITALSKILLED NURSING FACILITYWIDOWEDWHITE0F318283943.38445689071.092.076.333333110.0144.0122.833333141401111000000000000230.00.7
61007223926492410EW EMER.EMERGENCY ROOMHOME HEALTH CARESINGLEWHITE0F385669560.77024353085.0131.0106.272727131.0198.0164.909091151500101000100110000112.00.6
71007384722194617DIRECT EMER.CLINIC REFERRALDIEDMARRIEDOTHER1M3176825923.45037054067.084.078.500000103.0120.0112.750000151500000100000000101164.01.0
81008924429469323URGENTTRANSFER FROM HOSPITALDIEDMARRIEDUNKNOWN1F397295389.48364673079.0115.087.714286127.0191.0148.857143154-110111000000000000271.02.0
91009222723138040EW EMER.EMERGENCY ROOMSKILLED NURSING FACILITYNaNUNKNOWN0F3260295311.10681780059.078.069.33333380.0124.0102.833333151501000100000001000146.01.8

Last rows

subject_idhadm_idadmission_typeadmission_locationdischarge_locationmarital_statusethnicityhospital_expire_flaggenderstay_idlosageinpatient_strokemin_mbpmax_mbpavg_mbpmin_sbpmax_sbpavg_sbpfirst_gcslast_gcsgcs_changeafibhyperlipidemiadiabeteshypertensioncor_art_dperi_vasc_dcar_art_stentsmokingtiaheparin_ivantihypertensive_iv_non_tight_controlantihypertensive_iv_tight_controlinotropesantiplateletsanticoagantihypertensive_non_ivfirst_glufirst_cre
25491995061525208232EW EMER.EMERGENCY ROOMHOME HEALTH CAREWIDOWEDWHITE0F379993631.93560289092.0122.0105.666667136.0161.0152.666667131410000000000100000126.00.5
25501995638328981174EW EMER.EMERGENCY ROOMDIEDWIDOWEDWHITE1F314305312.51395889055.093.072.57142992.0112.0103.428571101000001100000100001114.01.0
25511996580228211098OBSERVATION ADMITTRANSFER FROM HOSPITALDIEDWIDOWEDWHITE1F3457045513.30342674049.099.065.09090955.0131.088.45454515150101010000010100084.02.3
25521998037329043585EW EMER.EMERGENCY ROOMHOMEMARRIEDWHITE0M320637291.06991959065.080.073.000000112.0136.0120.714286151500000000000000000119.00.9
25531998314529647630URGENTTRANSFER FROM HOSPITALHOMEMARRIEDWHITE0F332371391.69616954090.0116.0100.714286131.0173.0148.85714315150000100011000000093.00.6
25541998540927293537EW EMER.TRANSFER FROM HOSPITALCHRONIC/LONG TERM ACUTE CARESINGLEWHITE0M339174486.64820640081.092.085.666667111.0129.0117.66666715150000000010100000096.00.8
25551998568325452614EW EMER.EMERGENCY ROOMSKILLED NURSING FACILITYMARRIEDWHITE0M329279042.71540584069.0102.081.000000140.0172.0159.000000159-60111001001000100130.00.8
25561998770226568899OBSERVATION ADMITTRANSFER FROM HOSPITALREHABMARRIEDWHITE0M375400991.95603051042.0124.094.14285760.0166.0118.2857146159010100011000110092.01.1
25571998930529998115EW EMER.EMERGENCY ROOMREHABSINGLEWHITE0M386636330.96524323066.081.073.466667103.0136.0122.466667151500000100100001100106.00.7
25581999944226785317ELECTIVEPHYSICIAN REFERRALREHABDIVORCEDWHITE0M375692546.950370430105.0120.0110.400000136.0170.0156.000000111430000000000010000136.00.9